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Digital Inline Holographic Microscopy DIHM of Weakly-scattering Subjects

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Rate-Distortion-Based Stego: A Large-Capacity Secure Steganography Scheme for Hiding Digital Images.

Yi-Lun Pan^1,2, Ja-Ling Wu^1,3

¹Department of Computer Science and Information Engineering, National Taiwan University, Taipei 10617, Taiwan.

Entropy (Basel, Switzerland)

|July 27, 2022

Summary

This summary is machine-generated.

A new secure steganography system, RD-Stego, embeds multiple images and QR codes into color images with minimal distortion. This large-capacity system offers high embedding rates and robustness against attacks.

Keywords:

generative adversarial network image steganography information hiding mutual information rate-distortion

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Area of Science:

Computer Science
Information Security
Digital Image Processing

Background:

Steganography is vital for secure data hiding within cover media.
Existing methods face challenges in capacity, security, and robustness against attacks.
The need for advanced steganographic systems with high embedding capacity and resilience is critical.

Purpose of the Study:

To introduce a novel rate-distortion-based steganographic system (RD-Stego) for large-capacity secure information hiding.
To evaluate the system's embedding capacity, adaptability to chosen cover attacks, and model stability.
To provide theoretical underpinnings for the system's cost function using mutual information.

Main Methods:

Development of a rate-distortion-based steganographic scheme (RD-Stego).
Embedding multiple three-channel color images and QR codes within a cover color image.
Utilizing a mutual information-based approach for cost function design and regulation processes.
Experimental validation using publicly available benchmark datasets.

Main Results:

The RD-Stego system achieves high embedding capacity, up to 192+ bits-per-pixel (bpp).
The system demonstrates low visual distortion when embedding secret data.
Empirical evidence shows robustness against chosen cover attacks and no leakage of secret information.
The trained model exhibits stability.

Conclusions:

RD-Stego offers a significant advancement in secure, large-capacity steganography.
The system effectively balances embedding capacity, security, and robustness.
The rate-distortion and mutual information-based design provides a strong theoretical foundation for practical applications.